| The signal coordination control of urban road network regional intersection is of great significance to improve the overall traffic efficiency of urban road network and alleviate the traffic congestion in urban development.However,in the actual road network area,due to the influence of road network structure,traffic flow dispersion,intersection attribute difference and signal phase and other factors,the implementation of "general" and "one size fits all" signal coordination control strategy obviously does not meet the actual traffic demand,and there are some intersections that only need to take a single point control scheme.Therefore,in order to implement more effective and refined coordinated control strategy for urban traffic network,it is necessary to divide the whole network area into reasonable sub areas.In view of this,it is necessary to comprehensively consider the influence of many traffic factors,quickly and accurately identify the traffic state mode of regional road sections,calculate the correlation degree of intersections,divide the control sub areas according to the correlation degree,and dynamically adjust the divided sub areas according to the traffic state mode of the road network,so as to achieve the coordinated control of the intersection signals in the road network.The main research work of this paper is as follows:First of all,we use Numpy,Pandas and Matplotlib tools of Python to process,count and analyze the massive cross-section traffic flow data collected by the fixed detector on the Linux server platform,and obtain the characteristic laws of traffic flow operation and distribution,which provides the data basis for the following theoretical research and model verification.Secondly,aiming at the problems of random selection of initial clustering center,high recognition error rate and slow convergence speed existing in traditional FCM in traffic state pattern recognition,a CFSFDP-FCM traffic state pattern clustering and recognition algorithm based on K-proximity distance graph is proposed.The results show that the proposed algorithm is effective,and compared with a single FCM algorithm,the recognition error rate is reduced by 5.2%,the algorithm efficiency is improved by 23.7%,and has better stability.Thirdly,from the point of view of coordinated control of road network intersections,a dynamic division method of control sub areas based on improved Fast Newman algorithm is proposed.Compared with the Fast Newman algorithm based on only the adjacency matrix between intersections in the road network area,this method introduces the correlation degree between adjacent intersections.On the basis of considering the factors such as road flow,traffic state mode,vehicle flow dispersion,intersection signal period,spacing and other factors,it adopts the extreme dimensionless processing method to construct the comprehensive correlation degree model of dynamic subarea division,and the edge weight between intersections in road network subarea division is determined quantitatively,and the modular degree in Fast Newman algorithm is redefined,so that the control subarea division is more reasonable,detailed and accurate.Finally,according to the traffic data of the road network area investigated on the spot,use the VISSIM software to simulate and verify the intersection coordinated control timing scheme corresponding to each sub area division method.The results show that: using the sub area division method proposed in this paper,the travel time of the road section,the average delay of the intersection and the total evaluation index of the average number of parking times are all reduced to a certain extent,at the same time,the divided sub area of the road network control is more reasonable and accurate,and the division efficiency is higher,and the effect is better. |
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